Staff Machine Learning Engineer

Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day.

Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, Ping Wu , founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences.

Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta.

About the role:

Machine Learning Engineers at Cresta work across several high-impact AI initiatives. Final team placement is determined based on experience, strengths, and business needs.

Current focus areas include:

Agentic Assist: Lead and build next-generation agentic AI systems that augment contact center agents in real time. This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting techniques, a rapid prototyping mindset, and a proven ability to translate cutting-edge research into scalable, production-grade systems.

Agent & System Quality: Design evaluation frameworks and improve the reliability, robustness, and performance of LLM-powered agents. This includes diagnosing and mitigating failure modes such as hallucinations, retrieval errors, tool misuse, context drift, prompt brittleness, and multi-step reasoning breakdowns, while defining measurable quality metrics (e.g., accuracy, faithfulness, task completion, latency, and cost) for complex, non-deterministic systems.

Insights: Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data. This track focuses on building high-performance ML systems that process complex data, extract structured insights, and deliver real-time, actionable intelligence at scale.

Responsibilities:

Define and lead the technical vision for Cresta’s next-generation Agentic AI systems, including Agentic Assist and enterprise AI Agents.

Architect scalable, production-grade LLM systems that integrate reasoning, retrieval, planning, tool use, and real-time decision-making into cohesive, intelligent workflows.

Design and evolve multi-agent orchestration frameworks that combine RAG, structured knowledge, domain-adapted models, and automated actions.

Establish best practices for building robust, reliable, and cost-efficient LLM-powered systems in high-scale production environments.

Own evaluation strategy for complex, non-deterministic AI systems, including offline benchmarking, online experimentation, LLM-as-a-judge methodologies, and systematic failure analysis.

Proactively identify and mitigate agent failure modes such as hallucinations, tool misuse, retrieval errors, prompt brittleness, context drift, and multi-step reasoning breakdowns.

Define measurable quality standards (accuracy, faithfulness, task completion, latency, cost efficiency, robustness) and drive continuous system improvement.

Influence cross-team architecture decisions across ML, backend, and product engineering to ensure seamless integration of AI capabilities.

Mentor senior engineers, raise the technical bar, and contribute to long-term AI strategy and roadmap planning.

Translate cutting-edge research advances into practical, high-impact production systems.

Qualifications We Value:

Bachelor’s degree in Computer Science, Mathematics, or a related field; Master’s or Ph.D. strongly preferred.

7+ years of experience building and deploying machine learning systems in production, including deep hands-on experience with LLMs at scale.

Demonstrated leadership in architecting complex AI systems, particularly agentic or multi-step LLM workflows.